The pathogenesis of musculoskeletal disorders is closely associated with the cumulative damage and fatigue failure behavior of fibrous connective tissues under long-term repetitive loading. However, significant technological challenges remain in real-time dynamic monitoring of ligament fatigue life, particularly the lack of efficient computational mechanics modeling frameworks and precise assessment tools adaptable to real-world movement scenarios. The multimodal integrated framework for ligament fatigue life assessment was proposed in this study. First, the high-accuracy subject-specific musculoskeletal models were developed based on individualized medical imaging data. A coupled hyperelastic-viscoelastic constitutive model was incorporated to accurately characterize the nonlinear mechanical behavior of ligamentous tissues and their fatigue damage evolution under cyclic loading. Furthermore, by integrating continuum damage mechanics theory, a time-dependent cumulative damage evolution equation was established to systematically quantify the coupling relationship between fatigue failure probability and dynamic mechanical loading. In the data-driven prediction module, an innovative deep-learning model that integrates kinematic-dynamic coupling was developed. By integrating wearable inertial measurement units, the model enables real-time inversion of ligament loading force-fatigue failure states and prediction of fatigue life. This approach effectively overcomes the limitations of traditional mechanical modeling in long-term, multi-scenario dynamic monitoring, achieving high-precision and minimally invasive fatigue life evaluation of ligaments. The proposed computational framework breaks the static-loading constraints of conventional fatigue testing, achieving the dynamic biomechanical analysis and fatigue life prediction under real movement conditions. This work not only provides novel theoretical insights into the mechanisms and modeling of ligament fatigue damage, but also provides a generalizable tool for biomechanical injury prevention, rehabilitation planning, and soft tissue fatigue analysis in the musculoskeletal system.

This study presents a comprehensive review of the correlation between tibial acceleration (TA), ground reaction forces (GRF), and tibial bone loading, emphasizing the critical role of wearable sensor technology in accurately measuring these biomechanical forces in the context of running. This systematic review and meta-analysis searched various electronic databases (PubMed, SPORTDiscus, Scopus, IEEE Xplore, and ScienceDirect) to identify relevant studies. It critically evaluates existing research on GRF and tibial acceleration (TA) as indicators of running-related injuries, revealing mixed findings. Intriguingly, recent empirical data indicate only a marginal link between GRF, TA, and tibial bone stress, thus challenging the conventional understanding in this field. The study also highlights the limitations of current biomechanical models and methodologies, proposing a paradigm shift towards more holistic and integrated approaches. The study underscores wearable sensors’ potential, enhanced by machine learning, in transforming the monitoring, prevention, and rehabilitation of running-related injuries.

Background: Understanding the internal load characteristics of the knee joint is essential for investigating unilateral knee injuries associated with running. This study examined the differences in the location and magnitude of von Mises stress in the internal structures of bilateral knee joints during the stance phase of gait following 10 km running at submaximal speeds. Methods: A healthy male recreational runner participated in this study. We employed a synergistic approach, integrating subject-specific knee joint angles, reaction forces, and moments derived from musculoskeletal modeling to inform and drive the finite element (FE) modeling of running. This methodology ensured a detailed and accurate representation of knee joint mechanics. The peak stresses of the bilateral knee menisci, tibial cartilage, and five main ligaments were quantified using a FE model during the stance phase. Results: The meniscus, tibial cartilage, anterior (ACL), posterior cruciate ligament (PCL), medial (MCL), lateral collateral ligament (LCL) and experienced larger loads in the non-dominant limb across most phases of stance. Additionally, fatigue elevated the peak loading on the non-dominant limb’s ACL, PCL, and LCL during the gait stance phase but diminished the load on these ligaments in the dominant knee joint. For Patellar ligament (PL), the non-dominant side showed maximal stress at initial contact, while the dominant side dominated during the remaining stance phases. Conclusions: This proof-of-concept substantially enhances our understanding of the impact of running-induced fatigue on bilateral knee loading. It provides a detailed analysis of factors leading to unilateral knee overload during extended running. These insights are essential in formulating targeted strategies to reduce injury risks.

Single-leg landing (SL) imposes substantial mechanical demand on the patellar tendon, with peak patellar tendon force (PPTF) serving as a key metric for characterizing the internal mechanical environment of the tendon. This study integrates 3D modeling with high-resolution in vivo kinematics to quantify the patellar tendon moment arm (PTMA) and the PPTF, examining their biomechanical correlations and neuromuscular features. Minimal sex-related PTMA differences suggest comparable anatomical leverage during knee flexion across both sexes. In both sexes, PPTF was significantly positively correlated with the knee flexion angle at initial contact (IC) and significantly negatively correlated with the knee range of motion (ROM). Muscle network analysis showed lower clustering coefficients in high-frequency versus low-frequency bands. Reduced IC knee flexion and increased ROM attenuate patellar tendon mechanical demand. By incorporating individualized moment-arm analysis, this study provides a biomechanical basis for understanding patellar tendon loading during landing.

Background: Patients undergoing anterior cruciate ligament reconstruction (ACLR) are at high risk of osteoarthritis or secondary injuries, with abnormal knee contact forces (KCFs) identified as a key factor in joint degeneration. Traditional KCF assessment relies on expensive lab systems while advances in computer vision and AI now enable low-cost alternatives. However, currently available methods oversimplify knee mechanics and neglect compensatory movements, highlighting the urgent need for intelligent, real-time monitoring tools for personalized rehabilitation. Therefore, the aim of this study was to develop and validate an integrated, non-invasive framework for accurate KCFs prediction in ACLR patients during daily activities. We hypothesized that combining enhanced musculoskeletal modeling with a deep learning architecture incorporating spatiotemporal attention would improve the prediction accuracy across multiple movement tasks. Methods: This study simultaneously recorded three daily movements of 29 post-ACLR patients using both Vicon and OpenCap. Motion trajectories captured by Vicon were imported into OpenSim for musculoskeletal modeling and KCFs calculation. Dataset comprising OpenCap-derived kinematics and OpenSim-computed KCFs was used to train 3 learning models for the prediction of KCFs in ACLR patients across different movements. Results: Among three models, CNN-BiGRU-Attention model demonstrated the best predictive performance across all three movement tasks (R2walking = 0.973 ± 0.003, R2running = 0.982 ± 0.004, R2descending stairs = 0.951 ± 0.007). CNN and self-attention mechanism collectively enhanced the model's ability to capture key features in ACLR patients' movement data, thereby improving KCF prediction accuracy. Furthermore, for the three daily activities, all models showed superior KCFs prediction performance in running and stair-descent tasks compared to walking. Conclusion: The developed framework successfully achieved high-precision prediction of KCFs. This technological breakthrough not only provides a real-time quantitative tool for rehabilitation monitoring in patients with ACLR, but also facilitates a paradigm shift from static laboratory analysis to dynamic real-time monitoring, with broad application prospects in sports medicine, rehabilitation engineering.